An integrated-hull design assisted by artificial intelligence-aided design method

Abstract

To mitigate the high cost of integrated ship design, in this paper, we propose an innovative integrated hull design methodology utilizing artificial intelligence-aided design technology. The AIAD methodology leverages a deep neural network model constructed from multiple fidelities datasets to curtail the exponential growth in data requirements resulting from the proliferation of design variables caused by integration. Combining the proposed bump-free deformation method and optimization algorithms with the multi-fidelity deep network, the AIAD-based design approach facilitates the achievement of optimal hull design solutions with remarkable fidelity while minimizing design workload. Utilizing the developed design method, we performed a numerical test on integrated-hull design for container ships.Through a comparative analysis and sensitive analysis of design results and workload, the optimized results obtained demonstrated the effectiveness of the proposed AIAA integrated-hull design methodology in achieving optimal designs while preserving high fidelity and low workload. The proposed new approach has shown promises to expedite ship integrated-hull design, addressing the longstanding issue of its high cost.